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Policies significantly shape computation’s societal impact, a crucial HCI concern. However, challenges persist when HCI professionals attempt to integrate policy into their work or affect policy outcomes. Prior research considered these challenges at the “border” of HCI and policy. This paper asks: What if HCI considers policy integral to its intellectual concerns, placing system-people-policy interaction not at the border but nearer the center of HCI research, practice, and education? What if HCI fosters a mosaic of methods and knowledge contributions that blend system, human, and policy expertise in various ways, just like HCI has done with blending system and human expertise? We present this re-imagined HCI-policy relationship as a provocation and highlight its usefulness: It spotlights previously overlooked system-people-policy interaction work in HCI. It unveils new opportunities for HCI’s futuring, empirical, and design projects. It allows HCI to coordinate its diverse policy engagements, enhancing its collective impact on policy outcomes. 

Accounting for technologies’ unintended consequences—whether they are misinformation on social media or issues of sustainability and social justice—increasingly requires HCI to consider technology design at a societal-level scale. At this scale, public and corporate policies play a critical role in shaping technologies and user behaviors. However, the research and practices around tech and policy design have largely been held separate. How can technology design and policies better inform and coordinate with each other in generating safe new technologies? What new solutions might emerge when HCI practitioners design technology and its policies simultaneously to account for its societal impacts? This workshop addresses these questions. It will 1) identify disciplines and areas of expertise needed for a tighter, more proactive technology-and-policy-design integration, 2) launch a community of researchers, educators, and designers interested in this integration, 3) identify and publish an HCI research and education agenda towards designing technologies and technology policies simultaneously. 

Website privacy policies sometimes provide users the option to opt-out of certain collections and uses of their personal data. Unfortunately, many privacy policies bury these instructions deep in their text, and few web users have the time or skill necessary to discover them. We describe a method for the automated detection of opt-out choices in privacy policy text and their presentation to users through a web browser extension. We describe the creation of two corpora of opt-out choices, which enable the training of classifiers to identify opt-outs in privacy policies. Our overall approach for extracting and classifying opt-out choices combines heuristics to identify commonly found opt-out hyperlinks with supervised machine learning to automatically identify less conspicuous instances. Our approach achieves a precision of 0.93 and a recall of 0.9. We introduce Opt-Out Easy, a web browser extension designed to present available opt-out choices to users as they browse the web. We evaluate the usability of our browser extension with a user study. We also present results of a large-scale analysis of opt-outs found in the text of thousands of the most popular websites.